Displaying 4 results from an estimated 4 matches for "quandt".
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quand
2009 Sep 18
1
some irritation with heteroskedasticity testing
...eve, it isn't really increasing...
2. further I ran the following tests
bptest (studentized and non-studentized), gqtest, ncv.test with the
following results:
ncv:
Non-constant Variance Score Test
Variance formula: ~ fitted.values
Chisquare = 13.87429 Df = 1 p = 0.00194580
Goldfeld-Quandt test
data: reg
GQ = 1.7092, df1 = 327, df2 = 327, p-value = 7.93e-07
studentized Breusch-Pagan test
data: reg
BP = 15.8291, df = 23, p-value = 0.92
Breusch-Pagan test
data: reg
BP = 377.5604, df = 23, p-value < 2.8e-18
bptest and gq.test sport pretty straight forward examples saying the...
2006 Sep 29
6
List-manipulation
Hi,
Sorry for the question, I know it should be basic knowledge but I'm
struggling for two hours now.
How do I select only the first entry of each list member and ignore the
rest?
So for
> $"121_at"
> -113691170
> $"1255_g_at"
> 42231151
> $"1316_at"
> 35472685 35472588
> $"1320_at"
> -88003869
2009 May 12
0
R^2 extraction and autocorrelation/heterokedasticity on TSLS regression
...ar multiple regression on annually data which go from 1971 to 1997. After performing the TSLS regression, I tried to extract the R squared value using “output$r.squared” function and to perform autocorrelation (Durbin Watson and Breush Godfrey) and heterokedasticity tests (Breush-pagan and Goldfeld Quandt) but I have errors messages. More specifically, this is function that I write to R and below its response :
for R^2 :
> output$r.squared
NULL
for heterokedasticity tests :
>bptest(reg1)
Error in terms.default(formula) : no terms component
and for autocorrelation test, when I try :
durbin.wat...
2005 Jan 25
1
Threshhold Models in gnlm
Hello,
I am interested in fitting a generalized nonlinear regression (gnlr) model
with negative binomial errors.
I have found Jim Lindsay's package that will do gnlr, but I have having
trouble with the particular model I am interested in fitting.
It is a threshhold model, where below a certain value of one of the
parameters being fitted, the model changes.
Here is a sample:
Cones: